4 ms·

This is great thanks! I had a few weeks ago the problem of not being able to get a passive verb parser in CoreNLP fast enough to work. Does SpaCy support reduce

• syllogism 12y ago
  You can write rules to find them in the dependency parse, although the parse tree won't necessarily be correct. I've thought a lot about passive reduced relative clauses over the years --- they were a big part of my PhD thesis. So I happen to know that the first one in the WSJ data is wsj_0003.1. This isn't in the training or development data, but it's in the same data set --- so, this is a fair but optimistic spot-check. The sentence is: > A form of asbestos once used to make Kent cigarette filters has caused a high percentage of cancer deaths among a group of workers exposed to it more than 30 years ago, researchers reported. There are two reduced passives here --- "used" and "exposed", and a potential (but unlikely) false positive in "reported". spaCy correctly attached "exposed" to "workers", but didn't attach "used" correctly --- it attached it to "reported" instead of "form". This doesn't really make syntactic sense, but that's what it did --- the system's entirely statistical; there's no grammar licensing certain attachments. To see the parse, run: from spacy.en import English nlp = English() tokens = nlp(u'A form of asbestos once used to make Kent cigarette filters has caused a high percentage of cancer deaths among a group of workers exposed to it more than 30 years ago, researchers reported.') for word in tokens: print word.orth_, word.tag_, word.dep_, word.head.orth_
  • _glass 12y ago
    Thanks! spaCy seems to be really easy to use, but CoreNLP still yields better results, cf. http://nlp.stanford.edu:8080/corenlp/process http://nlp.stanford.edu:8080/corenlp/process